Modular Neuromusculoskeletal Model for Prosthetic Control
Toolbox (core tools used / referenced): Python, C++, VS Code; GitHub; ROS, Gazebo, PyBullet; Blender; MakeHuman / MB-Lab; URDF; NumPy / SciPy; plotting/visualization libraries; custom neural-input / spike-train modeling modules; documentation / write-up tools.
Overview / Abstract
This project aims to build a modular 3-D neuromusculoskeletal (NMS) simulation platform integrating skeletal dynamics, muscle–tendon mechanics, proprioceptive feedback, and optionally peripheral neural inputs. It is intended for prosthetic limb control and embodiment studies, embedded within a robotics/physics-simulation environment to enable end-to-end virtual testing of prosthetic control strategies. The framework will support experiments in prosthetic control, rehabilitation engineering, and neuro-musculoskeletal interaction — offering a flexible, reusable, physiologically grounded simulation sandbox.
Research Goals / Specific Aims
Develop a validated, modular NMS simulation framework capable of representing anatomy (bones, joints, muscle–tendon units), muscle dynamics, and neural/feedback loops in 3D.
Enable in-silico evaluation of prosthetic control algorithms — testing control strategies (EMG/neural-driven, adaptive control, feedback-based control) under realistic biomechanical and neurophysiological constraints before building hardware.
Characterize sensitivity and robustness of prosthetic control to anatomical and physiological variation (e.g. limb geometry, muscle properties, neural-feedback delay/noise, loading conditions) and assess effects on control performance and stability.
Provide a flexible, reusable platform for downstream research — modular outputs (anatomy, dynamics, control, feedback) that can be reused/adapted for related projects (adaptation studies, prosthetic design, biohybrid systems).
Document and publish the framework — including model parameters, assumptions, validation data, and limitations — ensuring transparency, reproducibility, and enabling peer review or community use.
Significance / Why It Matters
Direct experimentation on the human neuromusculoskeletal + neural-control system — especially with prosthetics — is difficult, costly, and limited in what internal states can be observed. Realistic simulation enables exploration of internal dynamics (muscle forces, tendon loads, neural drive, feedback) that are otherwise unobservable.
By evaluating control algorithms in simulation first, one can iterate design, test many variants, reducing time, cost, and risk associated with hardware prototyping.
Given human anatomical and physiological variability, a modular simulation allows customization to different anatomies or control scenarios — supporting personalized prosthetic control strategies and rehabilitation protocols.
Once validated, the framework can support related studies — biomechanics, adaptation, safety testing, stress/strain prediction, design evaluation — making it a versatile tool for computational neuro-biomechanics and prosthetics research.
Methods / Approach
Anatomical models (bone, joints, muscle–tendon geometry) defined via anatomical-modeling tools (meshes, rigging, skeletal structure). Musculoskeletal dynamics implemented by combining rigid-body skeleton dynamics with muscle–tendon mechanics, joint constraints, muscle activation/force generation; using numerical libraries for dynamic computations (force-length/velocity curves, tendon routing, activation dynamics, joint torques, etc.). Optionally integrate neural input and feedback modules: spike-train or neural-control models to simulate neural drive, proprioceptive feedback, reflex loops, sensory feedback, and interface to control algorithms or prosthetic control modules. Embed the neuromusculoskeletal model within a robotics/physics simulation environment (via ROS + physics engine or other simulation framework) to allow virtual interaction, simulate prosthetic control, and assess dynamics under movement and load. Validate the model by comparing outputs (joint torques, muscle activations, kinematics/kinetics, control outputs) against empirical data or literature-reported benchmarks for comparable movement tasks. Conduct sensitivity analyses — systematically vary anatomical, physiological, neural and control parameters (muscle strength, tendon stiffness, neural delay, feedback noise) to assess robustness, identify critical factors, and map the range of valid parameter space. Maintain modular code architecture (anatomy, dynamics, control, feedback, environment), version control (code + models), and documentation of assumptions, parameters, configurations, and limitations to ensure reusability and reproducibility.
Expected Deliverables & Outcomes
A working modular 3D NMS simulation framework (code + anatomical/dynamic models).
Documentation: model parameters, assumptions, usage instructions, validation results, known limitations.
Example simulation scenarios/demos: e.g. basic limb movement, prosthetic limb control, simple movement tasks — to showcase capabilities.
Sensitivity analysis reports: mapping how control performance and system stability vary with changes in anatomy, physiology, neural parameters, or control settings.
Optionally, a public code repository (if open-source) for sharing with collaborators or the community.
Foundation for downstream research: adaptation studies, prosthetic design testing, biohybrid prosthetic prototypes, etc.
Current Status & Next Steps (2023–Present)
Already done / in progress: anatomical model design; skeleton and musculoskeletal-dynamics modules have been developed.
Upcoming tasks: implement muscle–tendon dynamics + joint constraints + muscle activation/force generation; develop neural-input / feedback + control interface modules; integrate with simulation environment (physics/robotics simulator) for closed-loop testing (control → dynamics → environment → feedback).
Future plans: validate model on basic movement tasks; run comprehensive sensitivity analyses; finalize documentation and clean up code; prepare for sharing / collaboration or publication.
References / Links / Additional Resources
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